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The search for cotton (Gossypium hirsutumL.) genotypes gathering high yield and fiber quality is the main objective of breeding programs. The objectives of this study were: (a) to verify which effects contribute the largest fraction of the total phenotypic variance of cotton fiber quality traits and (b) to recommend the best genotypes for these traits. Eight competition trials of cotton cultivars were carried out between the 2017 and 2019 seasons. Field trials were carried out in Alagoinha (crops seasons 2017, 2018, and 2019) and Barbalha (2017 and 2018) in a randomized block design with three repetitions. Fifteen genotypes developed by the cotton plant breeding program of Embrapa Algodão were evaluated. The following fiber quality traits were evaluated: fiber percentage (FP), average boll weight (BW), upper‐half mean length, fiber uniformity (UNIF), short fiber index (SFI), fiber strength (STR), elongation (ELG), micronaire (MIC), and fiber maturity. Genotypic values (GVs) for the evaluated traits were estimated. The total phenotypic variance of the fiber quality traits evaluated is mostly composed of genotypic effects. Only the traits FP, BW, and ELG showed significant genotypes × sites interaction. The genotype G1 has high GVs for the traits FP, BW, fiber length, and fiber uniformity. The G15 stood out by gathering high values for UNIF, SFI, and STR. Cotton genotypes gathering high yield and fiber quality is the main objective of breeding programs.The total phenotypic variance of the fiber quality traits evaluated is mostly composed of genotypic effects.Only the traits fiber percentage, boll weight, and elongation showed significant genotypes—sites interaction.

Adapting production factors and relating them to the effects of rice milling can contribute to obtaining better quality grains in the industry. Thus, the study had an objective to evaluate the effects of cultivars and fertilization levels in the physical, physicochemical, and morphological quality of the polished and brown rice. The starch underwent minor changes in its structure with increased fertilization levels, conserving the physicochemical rice composition for cultivar IR 431 Cl in the polished rice. However, the combination of cultivar IR 424 Cl and brown rice demonstrated positive correlations in crude protein and starch. In addition, positive correlations between the physical variables were obtained in full milling. Furthermore, brown rice had the highest percentages of whole rice in the high fertilization levels, whereas the polished grain contained the highest percentage of broken and yellow grains in the low fertilization levels. The rice cultivar IR 431 Cl produced with high fertilization levels had high protein and starch quality in the polished grains, while the cultivar IR 424 Cl produced high quality in brown rice. We conclude that the association of cultivars and fertilization levels in rice production obtained high rice quality according to milling type. It is possible to define the best way of milling rice in the industry to enhance the final quality, depending on the production factors adopted in the crop, such as the type of cultivar and fertilization levels.

The performance of genotypes in a wide range of environments can be affected by extensive genotype × environment (G × E) interactions, making the subdivision of the testing environments into relatively more homogeneous groups of locations (mega‐environments) a necessary strategy. The genotype main effects + genotype × environment interaction biplot method (GGE) allows identification of mega‐environments and selection of stable genotypes adapted to specific environments and mega‐environments. The objectives of this study were to identify mega‐environments regarding sorghum [Sorghum bicolor(L.) Moench] grain yield and demonstrate that the GGE biplot method can identify essential locations for conducting tests in different mega‐environments. A total of 22 competition trials of grain sorghum genotypes were conducted over three crop seasons across several production locations in Brazil. A total of 25, 22, and 30 genotypes were evaluated during the first, second, and third crop seasons, respectively. After identifying the presence of G × E interactions, the data were subjected to adaptability and stability analyses using the GGE biplot method. A phenotypic correlation network was used to express functional relationships between environments. The GGE biplot was found to be an efficient approach for identifying three mega‐environments in grain sorghum in Brazil, selecting representative and discriminative environments, and recommending more adaptive and stable grain sorghum genotypes. Identifying mega‐environments in Brazil for sorghum planting, during the off‐season.Selecting representative and discriminative environments for sorghum in Brazil.Recommending more adapted and stable grain sorghum genotypes for the off‐season in Brazil.

The current challenge of corn (Zea maysL.) crops is to reach high yield to supply the world's demand for food, especially under different fertilization regimes and using the latest technology. We hypothesized that wavelengths and vegetation indices have a linear relationship with agronomic variables in corn. Our objectives were to verify the formation of super‐traits and to study the association between wavelengths and vegetation indices with agronomic traits in corn cultivated under high and low topdressing N. The experiments were carried out in two crop seasons using a randomized block design with three replicates in a factorial scheme (11 genotypes × 2 contrasting levels of N: low, 60 kg ha–1and high, 180 kg ha–1). At full bloom, the spectral variables green (550 nm), red (660 nm), red‐edge (735 nm), and near‐infrared (790 nm) and the vegetation indices normalized difference vegetation index (NDVI), normalized difference red‐edge index (NDRE), green normalized difference vegetation index (GNDVI), and soil‐adjusted vegetation index (SAVI) were evaluated. Agronomic traits evaluated were leaf N, plant height, first pod height, stem diameter, cob length, rows per cob, grains per row, and grain yield. Results showed that SAVI, GNDVI, NDVI, and NDRE and red‐edge and red were associated with agronomic traits in corn. The association between the agronomic traits evaluated here can be used to estimate the leaf N content and corn yield.

Reaction norms fitted through random regression models (RRM) have been widely used in animal and plant breeding for analyses of genotype × environment (G × E) interaction. However, in annual crops, they remain unexplored. Thus, this study aimed to evaluate the applicability and efficiency of RRM fitted through Legendre polynomials as a tool to recommend cotton (Gossypium hirsutumL.) genotypes. To this end, a data set with 12 genotypes of cotton evaluated in 10 environments for fiber length (FL) and fiber fineness was used. The restricted maximum likelihood/best linear unbiased prediction (REML/BLUP) procedure was used to estimate the variance components and to predict the genetic values. Results showed that there was genetic variability among cotton genotypes and that the reaction norms over the environmental gradient illustrated the G × E interaction. Very high selective accuracies (rĝg>0.90) were found for both traits in all environments, which indicates high reliability in the genotype's recommendation. The areas under the reaction norms were calculated for the recommendation of genotypes for unfavorable, favorable, and overall environments. Regarding genotypes recommendation, areas under reaction norms allow recommending genotypes for unfavorable and favorable environments, as well as for overall recommendation, for both traits. This study is the first considering reaction norms fitted through RRM for the recommendation of cotton genotypes and demonstrated the potential of this technique in cotton breeding, besides its great potential to deal with G × E interactions.

An accurate, efficient and informative statistical method for analyses of genotype x environment (G x E) interactions is a key requirement for progress in any breeding program. Thus, the objective of this study was to quantify individual variation in reaction norms using random regression models fitted through Legendre polynomials in eucalyptus (Eucalyptus spp.) breeding. To this end, a data set with 215 eucalyptus clones of different species and hybrids evaluated in four environments for diameter at breast height (DBH) and Pilodyn penetration (PP) was used. Variance components were estimated by restricted maximum likelihood, and genetic values were predicted by best linear unbiased prediction. The best-fitted model for DBH and PP was indicated by the Akaike information criterion, and the significance of the genotype effects was tested using the likelihood ratio test. Genetic variability between eucalyptus clones and very high accuracies (rg^ g > 0.90) were detected for both traits. Reaction norms and eigenfunctions generated genetic insights into G x E interactions. This is the first study that quantified individual variation in reaction norms using random regression models fitted through Legendre polynomials in eucalyptus breeding and demonstrated the great potential of this technique. [ABSTRACT FROM AUTHOR]

The search for high yield has identified ammonium toxicity as a problem in arable soils world wide. Ammonia toxicity can be suppressed by the use of silicon, but this fact still needs to be elucidated. Therefore, this review aimed to highlight the harmful effects of ammonium toxicity on model plants, and to determine the effects of Si on the mitigation of abiotic stress. Some plant species are considered as tolerant, and others as sensitive to high N concentrations. In sensitive plants, high ammonium concentrations may hinder the plant's development and even lead to the plant's death due to biochemical, physiological, and nutritional changes. Studies have demonstrated that silicon can mitigate or alleviate the deleterious effects caused by the toxic effect of NH4+. These findings were attributed to improvements in the physiological and nutritional parameters of plants. Given the importance of ionic balance between N forms for the plant's development, further studies must be performed to detect mechanisms promoted by Si to decrease or mitigate the harmful effects caused by excess ammonium in plants.

Understanding of how crop yield is influenced by interactions between the soil and environment is needed to minimize agricultural inputs and reduce the impacts of agriculture on the environment. The objective of this study was to determine the effect of soil chemical attributes, soil type, and rainfall on cotton (Gossypium hirsutumL.) yields and yield variability. In this study, the average cotton fiber yield was collected from 101 fields over nine crop seasons. In these fields, located in central Brazil, the average and standard deviation of normalized difference vegetation index (NDVI) were determined. Growing season rainfall and soil nutrients were measured. The variables analyzed were: average cotton fiber yield, NDVI from Landsat images calculated at the boll development phenological stage (enlargement and fiber filling phases), CV of the NDVI, precipitation during the cycle, and soil chemical attributes. The average field NDVI values collected at the boll development phenological stage were correlated to yield (r= 0.50; p< 0.05). Fields with a higher CV for the NDVI values generally had lower yields. The effect of precipitation depends on the cotton phenological stage and soil type. A high rainfall volume at the beginning of the cropping cycle increased plant height variability, which harmed the cotton fiber yield. The highest average values of aluminum content, aluminum saturation, and potential acidity promoted variability in cotton plant growth. Soil chemical attributes related to aluminum were negatively correlated with yield (r= −0.34; p> 0.05). Core IdeasAnnual averages of NDVI and yield presented a significant correlation.The effect of precipitation depends on the cotton phenological stage and soil type.Soil chemical variables related to aluminium are negatively correlated with the yield. Annual averages of NDVI and yield presented a significant correlation. The effect of precipitation depends on the cotton phenological stage and soil type. Soil chemical variables related to aluminium are negatively correlated with the yield.

A gain in association was obtained by using a new multivariate index with alleles of TaALMT1and TaMATE1Bgenes compared with a previously published index. Wheat is among the major grain crops. Its growth can be affected by aluminum toxicity, an important stress factor that limits plant productivity. Thus, an efficient means to select wheat genotypes that are resistant to this stress is required. Here, 172 wheat cultivars and 23 synthetic hexaploid wheat plants showing different alleles for TaALMT1and TaMATE1Band different levels of Al resistance in field conditions (phenotyped in two crop seasons) were studied. The modified Lin and Binns method was applied to obtain the phenotype data in a multivariate manner aiming at a general identification of genotypes based on the multiple traits evaluated. The genotype × year interaction was significant for all traits that were evaluated in three growth stages (tillering, silking, and maturation). The top 20 Al‐resistant genotypes obtained by the multivariate method were highlighted as having the best combination of the main desirable traits. Correlation analysis showed a gain in the association of the multivariate index with alleles of TaALMT1and TaMATE1Bgenes compared with a previously published index. The superior TaMATE1Ballele (having an insertion in the promoter region) contributed a greater Al resistance independent of the TaALMT1allele. The higher association with Al resistance qualifies the selection index described in this study to be used for wheat breeding programs with the aim of improving the performance of wheat when grown under Al stress. Core IdeasWheat is among the major grain crops.Its growth can be affected by aluminum toxicity.Genotypes with high adaptability and stability were identified.A new multivariate index allowed greater association with Al resistance. Wheat is among the major grain crops. Its growth can be affected by aluminum toxicity. Genotypes with high adaptability and stability were identified. A new multivariate index allowed greater association with Al resistance.

ABSTRACTBrazilian biomes are home to a significant portion of the world’s biodiversity, with a total of 14% of existing species and still concentrate 20% of the world’s water resources. However, changes in biomes have a direct impact on rainfall patterns and water recycling. Based on this, the objective was to evaluate the variability of rainfall in the four existing biomes in the Northeast Brazil (NEB) and their interaction with the ENSO climate variability mode and regional scale meteorological systems via CHELSA product. For this, monthly rainfall data were used from 1979 to 2013, with a spatial resolution of 1 km × 1 km of the CHELSA product, and seasonal and annual rainfall patterns were extracted via boxplot. It was found that the rainy season in the Amazon, Caatinga and Cerrado biomes occurred between January and April, with varying intensities, except for the Atlantic Forest. Such seasonality patterns are associated with the NEB meteorological systems, with emphasis on ITCZ (all Biomes), UTCV (Amazon, Caatinga and Cerrado), Frontal Systems (extreme south of Caatinga, Cerrado and Atlantic Forest) and EWD/TWD in the (Atlantic Forest). In the inter-annual scale, the remarkable influence of ENSO was verified, mainly in the years 1983, 1985, 1989, 1993, 1998, 2009 and 2012. It is noteworthy that 1985 was the wettest year of the period, with a surplus in all biomes, while the driest year differs between the Amazon (1983), Atlantic Forest and Caatinga (1993) and Cerrado (2012) biomes. The study via orbital product in NEB showed that anthropogenic processes and natural variability interfere with the forms of rain interception in the biomes and hence in rainfall patterns and water recycling in NEB.

An alternative to increase yield without raising the production costs and minimizing the dependence of agricultural inputs is the development of maize genotypes presenting high nitrogen (N) use efficiency under low N level conditions. Thus, the objective of this study was to estimate the specific combining ability of maize topcrosses under high and low nitrogen (N) conditions and to select highyielding hybrids for both conditions. The grain yield of 110 top-cross hybrids was evaluated in 2012 in two locations in the Brazilian Midwest. Partial diallel analysis was performed with the adjusted means of each of the individual top-cross analyses. Specific combining ability (SCA) of the hybrids and their interaction with N levels was estimated for each location. The mean grain yield of the 11 hybrids selected by the highest SCA estimates for high and low N was higher than the controls mean in approximately 330 and 241 kg·ha-1. These results demonstrate that the selection for each environment was efficient and reveals the possibility of developing new hybrids for the Brazilian Cerrado region. [ABSTRACT FROM AUTHOR]

The performance of textile processes and the quality of the products depends on the several cotton (Gossypium hirsutumL.) fiber quality traits, such as micronaire index, fiber length, and fiber strength. This research aims to recommend cotton genotypes for the Brazilian Cerrado based on fiber yield and fiber quality traits. Nineteen cotton cultivar variety trials were performed in the 2013–2014 and 2014–2015 crop seasons. Each trial was conducted as a randomized complete block design with 12 treatments and four replications. The evaluated traits were fiber yield, fiber length, fiber strength, and micronaire. For each trait, a modified Lin and Binns method was used to recommend the best genotypes for all environments (favorable and unfavorable). This method was also used in a multivariate manner, aiming at a general recommendation of genotypes based on the multiple traits evaluated. The genotype × environment interaction was significant for all evaluated traits. Genotypes with high adaptability and stability were identified for each trait. The multivariate method identified IMA 08 WS and BRS 335 as the genotypes that had the best combination of main desirable traits. The analysis further revealed that new crosses could be performed between genotypes with high yield adaptability and stability and genotypes with adequate fiber quality traits.

Cotton (Gossypium spp.) provides ~90% of the world’s textile fibre. The aim of this study was to use the principal additive effects and multiplicative interaction (AMMI) model under the Bayesian approach to recommend cotton genotypes for the Central-West region of Brazil. Eight trials with upland cotton genotypes were conducted during the 2008–09 harvest in the State of Mato Grosso, Brazil. The experiment included a randomised block design with 16 genotypes. The genotypes were evaluated for fibre yield, length and strength. Chains were simulated via the Markov chain Monte Carlo method with 300000 iterations for the parameters of the Bayesian AMMI model. From the chains generated, the first 20000 burn-in observations were discarded and samples were taken by jumping every 20 observations (thin). Bayesian analysis provided additional results to those obtained by the frequentist approach, highlighting the credibility regions in the biplot for the genotypic and environmental scores. Bayesian AMMI model allowed identification of a genotype that can be widely recommended; this genotype has genotypic values above the overall mean for the three evaluated traits and did not contribute to the genotype × environment interactions observed in these traits. In addition, adaptability of genotypes to specific environments was observed, which makes it possible to capitalise the positive effect of the genotype × environment interaction.

The use of microorganisms is a very recurrent reality in agriculture.Bradyrhizobium japonicumand Azospirillum brasilensebenefits the development of plants.There was an increase in the yield components of soybean. Microorganisms are widely used in agriculture. Among them, the bacterial genus Bradyrhizobiumis commonly found in cultivated soils, forming symbiotic relationships with legume species. This work aimed to evaluate whether co‐inoculation with Bradyrhizobium japonicumand Azospirillum brasilenseinfluences the components of production, yield, and physiological quality of soybean seeds. The experiment was conducted in three consecutive crop seasons (from 2014–2015 to 2016–2017), in the municipality of Chapadão do Sul, state of Mato Grosso do Sul, Brazil. The experiment consisted of a randomized complete block design, with seven treatments and four replications, using the following treatments: T1, control (without inoculation); T2, inoculation with B. japonicum(150 mL); T3, inoculation with A. brasilense(150 mL); T4, co‐inoculation with B. japonicum+ A. brasilense(75 mL + 75 mL); T5, co‐inoculation with B. japonicum+ A. brasilense(150 mL + 150 mL); T6, co‐inoculation with B. japonicum+ A. brasilense(225 mL+ 225 mL); and T7, co‐inoculation with B. japonicum+ A. brasilense(300 mL+ 300 mL). Each treatment (dose) was applied to 50 kg of soybean seeds. The study evaluated the agronomic variables number of nodules per plant, dry shoot biomass per plant, plant height, number of pods per plant, number of seeds per plant, mass of 100 seeds, and grain yield; and the seed quality variables first germination count, germination, emergence, emergence speed index, electric conductivity, accelerated aging, and tetrazolium. The co‐inoculation of soybean seeds with B. japonicumand A. brasilensebenefits the development of plants, increasing yield components, grain yield, and seed quality. The most efficient level was 225 mL of each inoculant for 50 kg of seed.

Spray deposition in corn crop is related to the vegetation indices.Spray deposition in corn crop is related to the spray application rate.Plant stand density has no influence on the spray deposition. Determination of the spray application rates (SAR) of crop protection products for different crops is subjective. The study aimed to develop an equation to estimate optimal spray application rates using a vegetation index (VI) and an expected relative spray deposition for summer corn crop. An experiment was performed in the crop year 2015 to 2016, and 2016 to 2017, with a plant density of 60,000 plants ha−1with 0.45‐m row spacing, under the no‐tillage system, in the municipality of Chapadão do Sul. An active multispectral sensor (Crop Circle ACS‐470) was used to obtain the VI Red Edge, Normalized Difference Red Edge (NDRE), and Normalized Difference Vegetation Index (NDVI), using the red, red edge, and near‐infrared bands. The experiment consisted of plots subdivided in a 4 × 4 factorial scheme (four spray application rates and four phenological stages), with four replications for each treatment. The mass balance analysis measured leaf deposits. The relationship between vegetation index values and middle stratum deposits showed a quadratic trend. When the vegetation index values are increased, the spray application rate (from 40 to 100 L ha−1) needs to also increase to maintain the same spray deposition (from 50 to 100%) in the middle stratum of corn plants. The vegetative indices studied (VI Red Edge, NDRE, and NDVI) positively correlated to the spray deposition in the middle stratum of corn plants, which also depends on the SAR. Furthermore, the relationship between vegetation index and spray deposits need to be studied for other crops, plant architecture, and stand density.

There are genotype × environment interaction for cotton in BrazilGenotype + genotype × environment interaction identified essential environments in Cerrado.Brazilian Cerrado is divided in two mega‐environments. In Brazil, cotton cultivars are tested annually in different environments at the final stages of breeding programs. Thus, identifying groups of environments with similar edaphoclimatic characteristics is crucial to determine the environments with higher discriminating ability of genotypes, known as essential environments. This work aimed to divide the Brazilian Cerrado environments into mega‐environments in relation to fiber yield of cotton genotypes and identify essential environments for the conduction of trials in each mega‐environment. Fiber yield was evaluated in 19 competition trials of cotton cultivars in the 2013–2014 and 2014–2015 seasons. The experimental design was a randomized block with 12 treatments and four replications. After verifying significant genotype × environment (G×E) interaction, mega‐environments and essential environments were identified using the genotype + G×E (GGE) biplot method. Two mega‐environments were detected. Mega‐environment 1 was formed by Santa Helena do Goias–Goiás State (GO), Primavera do Leste–Mato Grosso State (MT), and São Desidério–Bahia State (BA). Mega‐environment 2 was formed by: Trindade–Minas Gerais (MG), Campo Verde–MT, Pedra Preta–MT, Luiz Eduardo Magalhães–BA, Magalhães de Almeida–Maranhão State (MA), Teresina–Piauí State (PI), and Chapadão do Sul–Mato Grosso do Sul State (MS). Primavera do Leste in mega‐environment 1 and Pedra Preta in mega‐environment 2 are the optimal sites to conduct trials with cotton genotypes because due to high yield discriminating ability and more representative. This information is useful for breeders to plan the next steps for trials in the Brazilian Cerrado, minimizing resources, such as labor and trial time.

We used the AMMI Bayesian model for selection of cowpea genotypes.We identified two genotypes with more expressive genotypic values.It is not possible to carry out the general recommendation of cowpea genotypes for the Brazilian Midwest. Cowpea is one of the world's most important tropical and subtropical food sources. In Brazil, it is grown principally in three regions: the Midwest, the Northeast, and the North. This makes the genotype × environment (G×E) interaction a challenge on the recommendation of the best genotypes for these regions. The aim of this study was to use the additive main effects and multiplicative interaction model under the Bayesian approach to recommend the best genotypes for the Brazilian Midwest. Six trials using cowpea genotypes were conducted in the municipalities of Aquidauana, Chapadão do Sul, Dourados, and Primavera. The experimental design included a randomized block design with 20 treatments (17 lines and three cowpea cultivars) and four replications. We identified only two genotypes (G15 and G2) with more expressive genotypic values and whose credibility regions do not include zero. From the credibility regions built for the genotypic and environmental scores, we verify that these genotypes contributed to the G×E interaction, presenting specific adaptability to the subgroup of environments (E5, E6). These results indicate that it is not possible to carry out the general recommendation of high‐yielding cowpea genotypes for the Brazilian Midwest region.

Automation of mapping of soybean areas.Use of remote sensors in the recognition of summer crop.Development of exclusive vegetation index for soybean. The aim of this study was to map areas cultivated with soybean [Glycine max(L.) Merr.] in Paraná state, Brazil, using mono‐ and multitemporal MODerate‐resolution imaging spectroradiometer (MODIS) images. We applied the vegetation index perpendicular crop enhancement index (PCEI) and threshold determination for the automation of soybean area discrimination by geo‐object (GEOBIA). For this mapping, vegetation indices (normalized difference vegetation index [NDVI], enhanced vegetation index [EVI], and crop enhancement index [CEI]) and the development of the PCEI were used with the aid of time‐series images from the TERRA/MODIS system‐sensor. A support analysis, based on geo‐objects and a decision tree based on data mining, was used to determine the new vegetation index. “Classification” and “merge region” algorithms and feature extraction were used for classification. To evaluate the precision of the classifications, the Kappa (κ) and overall accuracy (OA) parameters were applied. Regarding the ground line, Rand R2were above 0.92 and 0.84, respectively (p< 0.01). The test results indicate that the proposed methodology is efficient for mapping soybean distribution, with 0.80 for the Kappa parameter, an appropriate crop spatial distribution, and no over‐ or underestimation of areas. Thus, this study allows automated mapping of areas cultivated with soybean crops at large scales.

The objective of this study was to investigate the association between the GGE Biplot and REML/BLUP methods and select cowpea genotypes that meet simultaneously high grain yield, adaptability and stability in the Mato Grosso do Sul environments. The experiments were carried out from February to July 2010, 2011 and 2012 in the municipalities of Dourados, Aquidauana and Chapadão do Sul. The experiments in Chapadão do Sul were conducted only in the years of 2010 and 2011, totaling eight environments. After detecting significant genotypes × environments (GE) interaction, the adaptability and the phenotypic stability of cowpea genotypes were analyzed by GGE Biplot and REML/BLUP methods. These methods were concordant in the identification of the best cowpea genotypes for the State of Mato Grosso do Sul. The BRS- Tumucumaque and BRS-Guariba cultivars are the closest to the ideal in terms of high grain yield and phenotypic stability, being suitable for cultivation in the State. [ABSTRACT FROM AUTHOR]

Copyright of Bragantia is the property of Instituto Agronomico de Campinas and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Bragantia is the property of Instituto Agronomico de Campinas and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Bragantia is the property of Instituto Agronomico de Campinas and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The Brazilian Legal Amazon is an extensive territory (5,088,668.25 km2) in which different factors (environmental and social) influence the fire dynamics of the region. This study aims to explain the seasonal patterns of meteorological variables, fire, land use, and carbon emissions and their inter-relationships, focusing on years of El Niño–Southern Oscillation (ENSO) occurrence. For this purpose, we used data from fire foci and burned area obtained by the Moderate Resolution Imaging Spectroradiometer sensor and meteorological variables from Reanalysis 2. The kernel density was applied to the fire foci, and Spearman correlation coefficient between the foci and the other variables (fire foci, burned area, carbon emissions, evapotranspiration, wind speed, relative air humidity, rainfall, soil moisture, maximum temperature, minimum temperature, and mean air temperature) and Mann Kendall test for soybean, corn and sugarcane crops were performed. The years considered as La Niña were those with the highest fire foci, burned area, and carbon emissions. Our results show that even in periods considered as low fire risk, forests may be vulnerable to fires due to interaction with other variables. Furthermore, we found a tendency to increase the area planted with soybean, maize, and sugarcane, which may lead to more deforested areas in the region if there is no support from public policies. The uncertainty of the Legal Amazon's behavior towards climate change, combined with possible setbacks in Brazilian environmental policy due to the current government, highlights the importance of studies that encompass several factors such as this one.

Brazil has the largest commercial beef cattle stock on Earth, and most of the cattle produced in the country is bred and finished on pastures. The cattle ranching sector represents a significant source of the country's greenhouse gas (GHG) emissions. Agricultural intensification has been highlighted as one of the main strategies in reaching global food security and reducing deforestation. The Sudden Death Disease (SDD) of pastures, which affects the most planted cultivar of Urochloa brizantha, is degrading pastures in the Amazon, contributing to low production yields and high emission rates. This paper discusses the intensification of pasture production systems and SDD, to examine the potential for pasture renovation to address livestock productivity and GHG balance, emissions and potential sinks. Does SDD represent a blessing or a curse to climate change mitigation in the Brazilian Amazon? A collection of pasture samples were assessed to measure wet and dry weight in areas with and without SDD, which were related to remote sensing data to provide an overall estimate of the total area affected by the SDD in Alta Floresta, a municipal county of southern Brazilian Amazonia. We found that 77.1% of all pastures had been committed to the syndrome, which has forced farmers to renew their pastures. This also has great potential in increasing soil carbon stocks, effectively reducing the CO2footprint of meat production in those areas. Therefore, we firmly believe that SDD management has provided an opportunity to rebalance the emissions/sequestration equation associated with meat production by the cattle ranching sector in this Amazonin frontier.

This study aimed to evaluate fire dynamics in ‘extreme’ climatic events of the El Niño Southern Oscillation in the western Amazon in Acre, Brazil. It employed remote sensing products of burned areas, the Enhanced Vegetation Index (EVI), rainfall products, and kernel density to spatialise fire foci. The largest burned area recorded during the study was during the La Niña period, with a total of 296 km2burned. The highest concentration of fire foci was found in the lowland open ombrophilous forest in areas with palm trees, where 28% of the fires occurred. Rainfall was concentrated on the cover of the submontane dense ombrophilous forests with emergent canopies with a maximum value of 2515.9 mm in the La Niña event. The highest and lowest EVI values occurred in the neutral episode with values ranging from 0.57 to 0.36. The correlation between burned and fire foci areas was greatest during the El Nino period (0.92). On the other hand, the maximum negative value was found between the rainfall and fires in the neutral episode (−0.80). Fire outbreaks and EVI presented a significant positive growth trend in the neutral episode. The largest fires were recorded during the neutral period, and this study highlights the vulnerability of the western Amazon to fire.